Method for the generation of synthetic images

ABSTRACT

The generation of images is done by the computation of the motion vectors representing the evolution from one image 2D to the other, directly from the co-ordinates and coefficients of geometrical transformation, of associated points in the 3D scene. The applications relate to the generation of monocular sequences by temporal interpolation and of stereoscopic sequences by extrapolation.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method for the generation ofsynthetic images, especially for the generation of image sequences.

The field of the invention is that of image synthesis by computer,namely the synthesis of monocular images for display on conventionalscreens and binocular or stereoscopic images for display in relief invirtual image type applications.

The aim of such methods is to reduce the cost of computation of theimage sequences generated.

2. Discussion of the Background

In the known approaches, the volume of computations required is reducedby a limiting, done homogeneously in the temporal and spatial domains,of the information elements processed by a rendition algorithm thatenables the preparing of a 2D image on the basis of structural datamodelling the 3D scene to be displayed. The diminishing of computationcosts then occurs inevitably to the detriment of the realism of thesequence thus generated.

The next step in the reduction of computation costs, which is also aknown one, is a temporal interpolation that recreates intermediateimages from key images, which are the only images computed on the basisof the rendition algorithm. The generation of such images is generallybased on the use of velocity vectors computed from one image to the nextone by means of these images and is therefore based on an estimate ofthe displacement of the pixels of the image.

In all these cases of interpolation, whether they are simple repetitionsof images or entail methods of greater complexity such as space/timefiltering operations, there are jerking and echo effects. These effectsarise out of the fact that neither the individual motion of objects inthe scene nor the motion of the observation point is taken into account.The temporal interpolation generates "estimated" images which aretherefore not always of good quality.

SUMMARY OF THE INVENTION

The present invention is aimed at reducing such deterioration ofsynthetic images.

To this end, and according to the invention, there is proposed a methodfor the generation of synthetic images from structural data modelling a3D scene to be displayed and data representing an apparent relativeevolution of the scene with respect to a viewpoint, the structural dataenabling the computation of the co-ordinates of points of the 3D sceneand the data on the evolution of the scene enabling the computation ofthe coefficients of geometrical transformation associated with thesepoints, at which there is generated, on the basis of the structural dataand a rendition algorithm, a 2D image representing the scene seen fromthis viewpoint, characterised in that, with each point of the 2D imagethere is associated a corresponding point of the 3D image and a motionvector representing the displacement of this point of the image due tothe apparent evolution of the corresponding point of the 3D scene withrespect to the viewpoint, said motion vectors of the pixels of the imagegenerated having been computed on the basis of the co-ordinates andcoefficients of the associated points of the 3D scene in order to usethem in the generation of at least one other 2D image on the basis ofthe first image, and then the generated images are shown on a displayscreen.

An object of the invention is also the use of fields of reverse apparentvelocity vectors for the temporal interpolation and of fields ofdisparity vectors for an extrapolation of an image generating astereoscopic pair of images. An object of the invention is also thegeneration of a stereoscopic sequence on the basis of the extrapolationand interpolation vectors.

The invention therefore relates to the generation of monocular sequencesas well as stereoscopic sequences.

The advantages obtained through this invention are a reduction of thecost of computing an image or a sequence of monocular or stereoscopicimages, improved image quality and therefore high realism of the sceneby means of a more reliable and simpler computation of the temporalinterpolation or spatial extrapolation vectors for a stereoscopic image,management of appearing and vanishing zones to achieve the interpolationon the basis of the final or initial image, management of conflicts ofprojection of the interpolation vectors as a function of the distancefrom the corresponding points of the 3D scene to the observation point,and a generation of one of the two stereoscopic channels on the basis ofthe extrapolation vectors of one channel.

To summarise: in the prior art, first of all at least two successive key2D images were generated by means of a rendition algorithm, and thenintermediate images were computed by an interpolation between two ofthese key images. This entailed major difficulties in making thecharacteristic points of the two images correspond with each other. Bycontrast, the invention proposes the generation of additional imagesfrom a key image obtained by the rendition algorithm and from motionvectors computed directly on the basis of data defining the evolution(rotation, translation motion, changes in focal length) of the 3D scene.

BRIEF DESCRIPTION OF THE DRAWINGS

Other features and advantages of the invention shall appear more clearlyfrom the following description, given by way of a non-restrictiveexample and made with reference to the appended figures, of which:

FIG. 1 represents the algorithm for the generation of a sequence ofmonocular images,

FIG. 2 shows the map of the apparent velocity vectors of a key imageK.sub.(t),

FIG. 3 shows a projection of a point of the 3D scene on the focal plane,

FIG. 4 shows the superimposition of "images" enabling a field ofvelocity vectors to be obtained,

FIG. 5 represents the algorithm for the computation of the temporalinterpolation,

FIG. 6 shows a projection of velocity vectors on the intermediate image,

FIG. 7 shows a conflict of projection on the intermediate image,

FIG. 8 shows a classification of "appearing" and "vanishing" zones,

FIG. 9 shows a stereoscopic sequence,

FIG. 10 represents an algorithm for the generation of the stereoscopicsequence,

FIG. 11 shows a masked zone of a key image of the right-hand channel,

FIG. 12 shows the determining of the disparity vector.

DISCUSSION OF THE PREFERRED EMBODIMENTS

The diagram of FIG. 1 shows an algorithm for the acceleration of imagesynthesis in the case of sequences of monocular animated images.

The exploited data, pertaining to the 3D scene that is the object of thesequence of animated images available at the input 1 of the algorithm,are information elements on the structure of the 3D scene of which amodel is made and on its kinematic values.

In a step 2, these information elements are processed by a renditionalgorithm to generate a limited number of images in the sequence knownas key images. A step 3 achieves the computation of a map of thevelocity and distance vectors on the basis of the animation parameters,which are the data on the kinematic values of the scene, and of the keyimages generated during the step 2. It thus assigns, to each pixel (i,j) of the key image K(t), an apparent velocity vector V⁺ (i, j, t)corresponding to the displacement of this pixel from the previous keyimage K(t-N) to this key image K(t) and an apparent velocity vector V⁻(i, j, t) corresponding to the displacement of this pixel from the imageK(t) to the image K(t+N) or more precisely the reverse of this motionvector. These velocity vectors may be likened to motion vectorsrepresenting the displacement of the pixels from one image to the nextone during the time interval between two consecutive key images.

FIG. 2 illustrates the definition of the two fields of the apparentvelocity vectors V⁺ and V⁻ representing the vector map of the key imageK(t). These vectors correspond to the apparent displacement between twokey images if the time unit is the interval between two key images. Thefield of vectors V⁻ (t) represents the displacement of all the pixels ofthe key image K(t) from the next key image K(t+N) up to the key imageK(t) and the field of vectors V⁺ (t) represents the displacement of allthe pixels of the key image K(t) from the previous key image K(t-N) upto the key image K(t).

For each pixel, this step also computes the distance from itscorresponding point in the 3D scene to the point at which the scene isseen, namely the fictitious camera that is supposed to film the scene.This value represents the minimum distance in the direction defined bythe pixel between the scene and the camera since, in the case of hiddenelements, it is the element closest to the camera that is taken intoaccount. This value may also come from the image generation step 2 whenthe technique for the generation of key images is based on methods ofthe depth buffer type which manage the projection of 3D information inthe image plane by the storage, for each pixel, of a value representingthis minimum distance. In this case, the distance map directly comesfrom the step 2.

Computation of the Apparent Speed

Before going to the next step 4, an explanation is given here below ofthe method of computation of the apparent speed of each pixel of a keyimage.

The apparent speed, namely the speed of displacement of the pixel of theimage in the image plane with reference to the point from which the 3Dscene is seen, is computed on the basis of the model of the 3D scene.

In theory, the apparent motion (2D motion) is the projection of the realmotion (3D motion) on the focal plane of the camera (image plane). Theprojection model most commonly used in image synthesis is the planeprojection model (perspective projection of the pinhole type).

Should another projection model be used, the formulation of thecomputation of the motion must be adapted accordingly.

In order to define the pinhole model, the following points andparameters are shown in FIG. 3:

    ______________________________________                                        P       the projection plane on which the 3D image is projected               (O, x, y, z)                                                                          the projection reference at the instant t, (centre O and axes                 Ox, Oy, Oz)                                                           f       the "focal distance" corresponding to the projection                          plane, namely the distance between the centre of projection                   O and the projection plane P                                          M (X, Y, Z)                                                                           the point of the 3D space, having coordinates X, Y, Z in                      the reference system (o, x, y, z)                                     m (x, y)                                                                              the projection of M in the projection plane, it being                         assumed that the projection plane P is perpendicular to the                   axis oz                                                               C       the projected point of the centre O in the plane P, defining                  an original reference system for the point m (x, y) whose                     coordinates are (x, y) with reference to this original                        reference system.                                                     ______________________________________                                    

The basis relationship of this projection model is: ##EQU1##

which is equivalent in the reference system (O, x, y, z) to: ##EQU2##

A first-order formulation is defined as follows:

A motion vector (dx, dy) is sought for each pixel as a function of thedisplacement (dX, dY, dZ) of the scene point M with respect to thecentre of projection and as a function of the variation of the focallength df.

The following equations (Eq. 1) are obtained by derivation: ##EQU3##

the distance Z is known for each pixel on the basis of the map of thedistances.

Having knowledge, on the basis of the animation information elements,namely the information elements pertaining to the kinematic values ofthe scene, of the geometrical transformation (R_(dt),T_(dt)) for a pointM_(o) of the 3D scene during the interval dt, it is possible to writethe following: ##EQU4##

with: ##EQU5##

By developing the above we obtain: ##EQU6##

The introduction of these values in (Eq. 1) gives: ##EQU7##

The set (dx,dy) represents the motion vector associated, according tothe invention, with each point (x,y) of the 2D image to enable thecreation of other images from this image (generated by the renditionalgorithm). The coefficients that come into play for the computation ofthe motion vector on the basis of the x and y co-ordinates of each pointare taken from the data defining the evolution (rotation, translation,variation of focal length) of the corresponding point in the scene to bedisplayed. These coefficients represent the relative apparent evolutionof the scene with respect to a viewpoint. It is a relative apparentevolution for not only can the elements of the scene evolve with respectto one another but also the focal distance and the relative position ofthe camera can evolve with respect to the scene.

It will be seen that the changes normally occur in time for the creationof the successive image sequences but, in a particular case, the changeswill be spatial for the creation of two images corresponding to one andthe same instant, seen from two different viewpoints. The motion vector(dx, dy) therefore has two possible uses which are very different fromeach other. These two uses may be combined. In the first use, it will becalled a "velocity vector" and in the second use it will be called a"disparity vector".

These formulae show that dx and dy may be split up into nine terms##EQU8## of which they are the linear combination.

Apart from the first term which is constant, these nine terms varyspatially, either in a manner that is totally determined in the image(for the first six terms) or as a function of the observed scene##EQU9##

The following are the coefficients:

    ______________________________________                                        N°      dx             dy                                              ______________________________________                                        1      1       γ .sub.x f                                                                             γ .sub.y f                                2      x                                                                                      ##STR1##      α.sub.y                                   3      y       β.sub.x                                                                                  ##STR2##                                       4      x.sup.2                                                                                ##STR3##      0                                               5      y.sup.2 0                                                                                             ##STR4##                                       6      xy                                                                                     ##STR5##                                                                                     ##STR6##                                               ##STR7##                                                                             fT.sub.x       fT.sub.y                                        8                                                                                     ##STR8##                                                                             -T.sub.z       0                                               9                                                                                     ##STR9##                                                                             0              -T.sub.z                                        ______________________________________                                    

The set of the first six terms (1 to 6) represents the contribution tothe apparent motion of an effect of rotation/zoom of the camera withrespect to the scene. Indeed, assuming that the point M is fixed, arotation of the projection reference system Ox, Oy, Oz simulating arotation of the camera modifies the x, y co-ordinates of the projectedpoint m(x,y). Similarly, a modification of the focal distance f betweenthe centre of projection O and the projection plane P, simulating avariation of focal distance of the camera, modifies the x, yco-ordinates of the projected point m of a fixed image point M(X,Y,Z).

The components of the motion vector dx, dy may be computed for eachpoint from the above formulae.

They may also be computed by general image processing methods: it ispossible to create images comprising x, y co-ordinate points and assigneach point a "luminance" value and a "colour" value. For example, theluminance value represents the contribution of the rotation/zoom (terms1 to 6 of the table) while the colour represents the contribution of theterms 7 to 9. By comprehensive image processing methods, it is possibleto make a comprehensive computation, for all the x, y points, of thevalues of the motion vectors.

Thus, to assess the contribution of the rotation/zoom effect, it ispossible to perform a linear combination with weighting operationscorresponding to the above table between six images, the luminance ofwhich is a refined function of 1, x, y, x², y² and xy.

This procedure is shown in FIG. 4.

The last three terms (7 to 9) of the above table are not zero in thecase of a 3D translation between the scene and the camera. In this case,the distance information relating to each point of the 3D scene to beshown must be exploited as explained here below.

In the case of a static scene, observed by a moving camera, the valuesof T_(x), T_(y) and T_(z) are constant for all the points of the sceneand represent the translation shift of the projection reference, itbeing assumed that the projection plane P is linked to the projectionreference.

To obtain the contribution of this translation motion to the field ofapparent motion, it is necessary to carry out a linear combination ofthree image signals whose luminance is set up respectively on the basisof the information 1/z, x/z and y/z for each pixel.

The image 1/z may be obtained on the basis of the information containedin a memory of the distances, hereinafter called Z-buffer, by theapplication of an appropriate conversion memory. ##EQU10##

are obtained respectively by multiplying this image ##EQU11##pixel-by-pixel, with the images x and y.

Should the scene be dynamic, the value of T_(x), T_(y) and T_(z) are nolonger constant for the points of the scene and an image synthesis onthe basis of the 3D model of the scene is necessary. It is necessary tocarry out a step for the assigning of a 3D translation field to thepoints of the scene, followed by a step for the projection of these 3Dtranslation motions in the image plane. For this purpose, a "colour" isassigned to each peak of a scene, this colour representing theindividual translation motion of this peak in space. The propagation ofthis peak information to the entire scene is approximated by a linearinterpolation of the colour on the surface of the objects, for exampleby using a "Gouraud" type algorithm.

In order to obtain the terms ##EQU12## the "colour" information hereabove, assigned in the 3D space, is first of all projected in the imageplane by means of an image synthesis projective algorithm similar to therendition algorithm. Then the signal is multiplied by the signals##EQU13## and weighted by the appropriate coefficients in order toobtain the terms 7 to 9.

By these combinations of images bearing the values of the constituentterms of the modulus of the apparent velocity vectors, the velocityvectors of the key image are deduced. This is done if the time intervaldt is the one that is between two key images. If not, it is alwayspossible to perform calculations corresponding to this interval byassuming that the motion between two key images is linear and atconstant speed. The vectors are assigned to the pixels of the key imageK(t) in considering the 3D scene at this instant t and at the instantt+dt for the vectors V⁻ and t-dt for the vectors V⁺. For the vectors V⁺,it is the geometrical transformation making it possible to go from theinstant t-dt to the instant t that is used for each of the points of thescene corresponding to the pixels of the key image K(t).

Let us now return to the algorithm of FIG. 1. The temporal interpolationstep 4 which follows the step 3 just described is described in detailhereinafter. It enables the creation of the missing intermediate imagesbetween two key images. At the step 5, these intermediate images arethen inserted between the key images generated at the step 2 to obtainthe monocular sequence of images.

FIG. 5 gives a detailed view of the step 4 of FIG. 1, relating to thetemporal interpolation, at the steps 4a to 4e. A step 4a defines theintermediate image that will be reconstituted. This image is calledI(t-n) located between two key images called K(t) and K(t-N).

The step 4b carries out a projection of the velocity vectors V⁺ (t) ofthe key image K(t) in their opposite direction on the intermediate imagedefined and on the key image K(t-N) and a projection of the velocityvectors V⁻ (t-N) of the key image K(t-N) in their opposite direction onthe intermediate image and on the key image K(t). The projections areshown in FIG. 6 for an image point P1 of the image K(t-N) and an imagepoint P2 of the key image K(t).

The pixels of the intermediate image corresponding to the projectedvectors V⁻ and V⁺, namely I1 and I2, are the pixels closest to theprojection points, the image being defined on a grid of pixels.

Thus, if N INT (NEAREST INTEGER) designates the nearest integer valueand therefore corresponds to a point of the spatial sampling grid, wehave: ##EQU14##

I₁, I₂, P₁ and P₂ are the co-ordinates of the points on the grid, V⁻ andV⁺ are the modules of the velocity vectors.

This step 4b also consists, during this projection, of the management ofthe conflicts.

The two fields of vectors V⁺ (t) and V⁻ (t-N) are scanned methodically.There is a conflict when two significantly different velocity vectorsare projected to one and the same point I of I(t-n). This occursespecially when there is a fine object in the foreground that isdisplacement (relatively) with respect to the background. It is thennecessary to choose the vector corresponding to the object closest tothe camera, in this case the object in the foreground. The informationon distance from the camera being known at each pixel of the key images,it is consequently also associated with each projected velocity vector.The vector bearing the smallest distance information (Z) is thus chosenand this distance information is stored at the point I so as to settlefuture conflicts if any at this same interpolated point. In the exampleof FIG. 7, the object A is closer to the camera than B (Z(A)<Z(B)). Itis therefore the value D(A) that is actually projected at I.

When the two fields of vectors have been entirely scanned, it ispossible that there will remain pixels of I(t-n) without any projectedvelocity vector. They are assigned a velocity vector by interpolation ofthe vectors projected in the neighbourhood.

The step 4c then consists of a classification of the interpolatedpixels.

The pixels of the intermediate image I(t-n) are divided into threeclasses, depending on whether they are visible in the two key imagesK(t-N) and K(t) (i.e. "standard" pixels), visible in K(t-N) and maskedin K(t) ("vanishing" pixels) or else, on the contrary, masked in K(t-N)and visible in K(t) ("appearing" pixels). The labelling of the pixelsI(t-n) is done in two stages:

Labelling of the key images by the reverse projection, along their path,of V⁺ (t) and V⁻ (t-N) on K(t-N) and K(t) respectively: as shown in FIG.8, the pixels without projected vectors ("holes") correspond to thezones (Zd, FIG. 8a) which will be masked for K(t-N) (vanishing pixels)and to the zones (Za, FIG. 8b) which will be unmasked for K(t)(appearing pixels) (we have taken the example of a fixed object in theforeground and a moving background).

Labelling of the intermediate image by the examination of the label ofthe zones K(t) and K(t-N) at which the vectors actually projected onI(t-n) are aimed. This amounts to projecting the "appearing" and"vanishing" labels on the intermediate image.

The next 4d step computes the luminance values of the pixels I of theintermediate image by the interpolation of the luminance values of thepixels P and S of the images K(t-N) and K(t) corresponding to theprojection vector chosen for I.

In terms of luminance:

    I=α×P+(I-α)×S

where α takes the following values:

α=n/N if I is labelled "normal".

n and N being defined by the fact that the n^(th) intermediate imagebefore K(t) is considered and that the key images K(t) and K(t-N) areseparated by (N-1) intermediate images.

α=0 if I is labelled as "appearing".

α=1 if I is labelled as "vanishing".

Finally, the last step 4e enables the management of the interpolationerrors.

In low-cost fast synthesis applications, it is not possible to predictinterpolation errors if any unless the rendition algorithm is used toverify it. These errors are foreseeable firstly in the zones where ithas been necessary to fill a hole in the field of velocity vectors. Thisresults in a failure of the simple model, based on displacement andstructure, that is used. These zones of uncertainty can be extended toall the pixels of the intermediate images for which V⁺ (t) and V⁻ (t-N)are not consistent. An estimated mask of the errors is deduced therefromand the rendition algorithm is applied to synthesise the correspondingpixels.

The foregoing method and computations may also be exploited to generateimages for a binocular or stereoscopic sequence.

FIG. 9 shows the key and intermediate images generated for a left-handsequence and for a right-hand sequence, these two stereoscopic channelsenabling the display to be viewed in relief.

The generation, for example, of the left-hand sequence is identical tothe generation of the monocular sequence as described here above. In thefigure, two intermediate images are interpolated between two consecutivekey images shown in black, and the gain in computation time is then in aratio of three with respect to the left-hand image. The key images ofthe right-hand sequence are vertically hatched. These images areextrapolated from the key images of the left-hand sequence as explainedfurther below and the intermediate images are computed on the basis ofthese new key images. If the time corresponding to the extrapolation isnegligible as compared with the time for the implementation of therendition algorithm enabling the generation of the key images of theleft-hand channel, the gain in computation time then corresponds to aratio of six in the example presented.

This method of accelerating the generation of binocular sequences ofsynthetic images by extrapolation is described by means of the diagramof FIG. 10.

Using data of the 3D scene model and its kinematic values available atthe input 11, a computation is made of the key images, the fields ofapparent velocity vectors and of distance corresponding to these imagesfor the monocular sequence, for example the left-hand sequence, in thesame way as in the steps 2 and 3 of FIG. 1 for a generation of monocularsequences, in this case the step 12. It therefore makes use, inter alia,of the rendition algorithms for the generation of the key images.

A step 13 processes the data computed at the step 12 and those of the 3Dmodel available at the input 11 to perform a computation of vectorscalled disparity vectors.

A real point of the 3D scene is projected at points having differentco-ordinates in the right-hand image and the left-hand image. Indeed,the centre of projection O' used for the right-hand image is offset withrespect to the centre of projection O used for the left-hand image. Thisoffset corresponds to an apparent offset of the static 3D scene withrespect to the centre of projection O. This is what creates thestereoscopic effect and, for each pixel having co-ordinates i_(g), j_(g)of the left-hand image, there corresponds a pixel having co-ordinatesi_(d), j_(d) of the right-hand image. The disparity vector is the vectorof translation of a pixel with co-ordinates i_(d), j_(d) towards thepixel with co-ordinates i_(g), j_(g). It is a motion vector assigned toeach pixel of the original image representing the evolution of the imageduring the displacement of the projection point.

In another way, it can be said that the binocular disparity vectorD(i_(g), j_(g)) assigned to the key image of the left-hand sequencebrings the projections of the same real point of the 3D scene intocorrespondence in the two 2D views. It is this disparity vector thatenables the right-hand key image to be extrapolated from the left-handkey image.

The computation of these disparity vectors is deduced very simply fromthe computations of the apparent velocity vectors as described hereabove.

Indeed, the disparity vector corresponds to two views of the same 3Dscene but at different angles to achieve the stereoscopic effect.

These two views correspond to an apparent displacement of the 3D scenewhich is fixed and therefore static. This displacement corresponds tothe two viewpoints.

Thus, it is enough to compute the apparent velocity vectors for a staticscene that has undergone this displacement, according to the methodexplained here above during the computation of the velocity vectors ofthe key image of a monocular sequence. These velocity vectors are thefictitious velocity vectors enabling the computation of the right-handimage on the basis of the left-hand image. These images correspond toone and the same instant t and not to different instants as in the caseof real velocity vectors. It is thus possible to compute the disparityvectors of all the image points at an instant t.

All the disparity vectors for a key image form the disparity field. Onthe basis of this field and of the corresponding key image, adisparity-compensated extrapolation is carried out in the step 14.

The extrapolation of the right-hand image Id(t) from the left-hand imageIg(t) and the disparity field D(t) of this image is explained withreference to FIG. 11.

Each pixel of Ig(t) is projected in the reverse direction of thedisparity vector on the image Id(t). Should there be conflict, i.e. ifseveral pixels of Ig(t) should be projected on one and the same point,the choice of the pixel closest to the camera is done as seen here inrelation to the generation of a monocular image. The distance mapassociated with the key images enables this selection.

The pixels of the image Id(t) at which no projection arrives, as shownin FIG. 11, normally correspond to the zones seen in Id(t) and masked inIg(t). They are computed at the step 15 by the rendition algorithm thathas enabled the generation of Ig(t). These reconstituted pixels areadded, in the step 16, to the image obtained in the step 14.

To the key images of the right-hand sequence thus available in the step16, there are added the velocity vector maps computed in the step 17.The field of velocity vectors Vd(t) between two consecutive right-handkey images Id(t-N) and Id(t) is deduced from the field of velocityvectors Vg(t) between two consecutive left-hand key images Ig(t-N) andIg(t) and from the binocular disparity fields D(t-N) and D(t) of thesekey images by the relationship symbolised by FIG. 12:

    Vg(t)-Vd(t)+D(t-N)-D(t)=0

In practice, assuming that the disparity of a pixel varies slightly fromone key image to the other, the apparent speeds of two points made tocorrespond in the right-hand and left-hand views by the disparity vectorare almost equal. Thus, the map of the velocity vector and distancevector of the right-hand key image may be deduced from that of thevectors of the left-hand image and from its disparity field.

The step 18 then performs the temporal interpolation according to thealready explained method. This interpolation is also performed in thestep 19 on the left-hand channel so that, by the juxtaposition at thestep 20 of these image sequences thus generated with the key images, thesequence is obtained stereoscopically.

The computation method used to determine the displacement values havebeen given herein by way of an indication.

Thus the velocity vectors could equally well be computed on the basis ofthe data for the modelling of the scene at two instants t and t+dt.

We claim:
 1. Method for the generation of synthetic images fromstructural data modeling a 3D scene to be displayed and datarepresenting an apparent relative evolution of the scene with respect toa viewpoint, the structural data enabling the computation of theco-ordinates of points of the 3D scene and the data defining theevolution of the scene enabling the computation of the coefficients ofgeometrical transformation associated with these points, at which thereis generated, on the basis of the structural data and a renditionalgorithm, a 2D image representing the scene seen from this viewpoint,characterized in that, with each point of the 2D image, there isassociated a corresponding point of the 3D image and a motion vectorrepresenting the displacement of this point of the image due to theapparent displacement of the corresponding point of the 3D scene withrespect to the viewpoint, said motion vectors of the pixels of the imagegenerated having been computed on the basis of the co-ordinates andcoefficients of the associated points of the 3D scene in order to usethem in the generation of at least one other 2D image on the basis ofthe first image, and then the generated images are shown on a displayscreen wherein the data defining the evolution comprise data on theevolution of the viewpoint of observation of the 3D scene correspondingto an apparent displacement of the static scene defined at an instant t,and in that a second image is computed by spatial extrapolation on thebasis of a reference image that is generated from the renditionalgorithm and corresponds to a viewpoint of the scene at the instant t,and on the basis of the motion vectors called disparity vectors andcalculated from the said data, this second image corresponding to asecond viewpoint of the scene at the same instant t to form astereoscopic view.
 2. Method according to claim 1, characterised in thata binocular sequence is formed by a reference channel constituted by keyimages generated on the basis of the rendition algorithm andintermediate images formed by temporal interpolation, and a conjugatedchannel constituted by key images formed by extrapolation of the keyimages of the reference channel of the basis of their disparity vectorsand intermediate images formed by the temporal interpolation of thesekey images for the achievement of the stereoscopic effect.
 3. Methodaccording to claim 2, characterised in that the velocity vectors of akey image of the conjugated channel are computed on the basis of thevelocity vectors and disparity vectors of the corresponding key image ofthe reference channel.
 4. Method according to claim 3, characterized inthat the zones in the conjugated image not placed in correspondence bythe disparity vectors are generated by the rendition algorithm. 5.Method according to claim 2, characterized in that the zones in theconjugated image not placed in correspondence by the disparity vectorsare generated by the rendition algorithm.
 6. Method according to claim1, characterised in that the zones in the conjugated image not placed incorrespondence by the disparity vectors are generated by the renditionalgorithm.
 7. Method for the generation of synthetic images fromstructural data modeling a 3D scene to be displayed and datarepresenting an apparent relative evolution of the scene with respect toa viewpoint, the structural data enabling the computation of theco-ordinates of points of the 3D scene and the data defining theevolution of the scene enabling the computation of the coefficients ofgeometrical transformation associated with these points, at which thereis generated, on the basis of the structural data and a renditionalgorithm, a 2D image representing the scene seen from this viewpoint,characterized in that, with each point of the 2D image, there isassociated a corresponding point of the 3D image and a motion vectorrepresenting the displacement of this point of the image due to theapparent displacement of the corresponding point of the 3D scene withrespect to the viewpoint, said motion vectors of the pixels of the imagegenerated having been computed on the basis of the co-ordinates andcoefficients of the associated points of the 3D scene in order to usethem in the generation of at least one other 2D image on the basis ofthe first image, and then the generated images are shown on a displayscreen wherein the data defining the evolution comprise data on thetemporal evolution of the 3D scene, and in that an intermediate image iscomputed by temporal interpolation on the basis of a reference imagethat is generated by the use of the rendition algorithm and thatcorresponds to the 3D scene at the instant t, and on the basis of themotion vectors called velocity vector and calculated from the said data,said intermediate image corresponding to a different instant and whereina sequence of monocular images is constituted by key images generated onthe basis of the rendition algorithm of the 3D scene and of intermediateimages achieved by temporal interpolation, on the basis of these keyimages and of the computed velocity vectors.
 8. Method according toclaim 7, characterised in that it attributes, to each key image K(t), afirst field of apparent velocity vectors V⁻ whose reverse vectorsdefine, for each pixel of the key image K(t), its displacement from thisimage to the next key image K(t+N), a second field of apparent velocityvectors V⁺ defining the displacement of these pixels from the previouskey image K(t-N) to the key image K(t), these vectors being computed onthe basis of the data relating to the structure and evolution of the 3Dscene, a distance map associating, with each pixel of the key imageK(t), the distance from the corresponding visible point of the 3D sceneto the point from which the scene is seen, and in that the interpolationbetween two key images K(t-N) and K(t) is done by reverse projection ofthe vectors V⁺ of the key image K(t) on the intermediate image and thekey image K(t-N) and by reverse projection of the vectors V⁻ of the keyimage K(t-N) on the intermediate image and the key image K(t).
 9. Methodaccording to claim 8 characterised in that, during the temporalinterpolation of an intermediate image I(t-n) between two key imagesK(t-N) and K(t), a conflict of projection between vectors V⁺ of theimage K(t) or between vectors V⁻ of the image K(t-N) on the intermediateimage I(t-n), i.e. the assignment of several vectors to one and the samepixel of the intermediate image, each vector being assigned to theclosest pixel of its projection on the intermediate image, is resolvedby choosing, as the chosen vector, that one whose origin is the pixel ofthe key image having the shortest distance, and in that the eliminatedvectors are not taken into account for the temporal interpolation ofthis intermediate image.
 10. Method according to claim 9, characterizedin that, during the temporal interpolation of an intermediate imageI(t-n) between two key images K(t-N) and K(t), a first step defines"appearing" and "vanishing" zones for each key image, the appearingzones on k(t), corresponding to zones or pixels not aimed at by theinverted apparent velocity vectors V⁻ of the image K(t-N), the vanishingzones on K(t-N) corresponding to zones or pixels not aimed at by theinverted apparent velocity vectors V⁺ reverse of the image K(t), thevectors V⁺ that reach the appearing zones and the vectors V⁻ that reachthe vanishing zones being labelled as a function of these zones, asecond step defines appearing and vanishing zones of intermediate imageas corresponding respectively to the projections, on this intermediateimage, of the vectors V⁺ and V⁻ labelled as being appearing andvanishing, a third step uses these zones in the temporal interpolationof this intermediate image.
 11. Method according to claim 10,characterized in that the appearing and vanishing zones of theintermediate image I(t-n) are generated respectively and exclusively onthe basis of the corresponding appearing zones of the key image K(t) andof the corresponding vanishing zones of the key image K(t-n), the otherso-called "normal" zones being interpolated by taking account of thezones corresponding both to K(t-N) and K(t).
 12. Method according toclaim 9, characterized in that the management of interpolation errors isdone by means of an estimated mask of the interpolation errorscorresponding to the zones of the intermediate images for which thevectors V⁺ and V⁻ are not consistent.
 13. Method according to claim 8characterised in that, during the temporal interpolation of anintermediate image I(t-n) between two key images K(t-N) and K(t), afirst step defines "appearing" and "vanishing" zones for each key image,the appearing zones on k(t) corresponding to zones or pixels not aimedat by the inverted apparent velocity vectors V⁻ of the image K(t-N), thevanishing zones on K(t-N) corresponding to zones or pixels not aimed atby the inverted apparent velocity vectors V⁺ reverse of the image K(t),the vectors V⁺ that reach the appearing zones and the vectors V⁻ thatreach the vanishing zones being labelled as a function of these zones, asecond step defines appearing and vanishing zones of intermediate imageas corresponding respectively to the projections, on this intermediateimage, of the vectors V⁺ and V⁻ labelled as being appearing andvanishing, a third step uses these zones in the temporal interpolationof this intermediate image.
 14. Method according to claim 13,characterised in that the appearing and vanishing zones of theintermediate image I(t-n) are generated respectively and exclusively onthe basis of the corresponding appearing zones of the key image K(t) andof the corresponding vanishing zones of the key image K(t-n), the otherso-called "normal" zones being interpolated by taking account of thezones corresponding both to K(t-N) and K(t).
 15. Method according toclaim 13, characterized in that the management of interpolation errorsis done by means of an estimated mask of the interpolation errorscorresponding to the zones of the intermediate images for which thevectors V⁺ and V⁻ are not consistent.
 16. Method according to claim 8,characterised in that the management of interpolation errors is done bymeans of an estimated mask of the interpolation errors corresponding tothe zones of the intermediate images for which the vectors V⁺ and V⁻ arenot consistent.